The surface recombination of nitrogen atoms in afterglow is studied by the time delay method, accompanied by the macrokinetic diffusive model. The method consists of the measurement of the dependence of the mean value of the breakdown time delay on afterglow period td=f(τ) and fitting of the data by the model that was developed. Excited N2(A 3∑+u) nitrogen molecules formed in the surface-catalyzed recombination on cathode produce secondary electrons. The electrons entering the interelectrode space determine the time delay in electrical breakdown. The time delay method is very efficient in nitrogen atom detection down to a natural radioactivity level. By fitting the calculated curve to the experimental data, we have: (1) shown that the nitrogen atom recombination on the glass container walls is second-order in N while the recombination on the copper electrode is the first order; (2) determined the value of the surface recombination coefficient for molybdenum glass; (3) determined the combined probability of N2(A 3∑+u) metastable formation by recombination at electrode surface and of secondary electron emission. Furthermore, we derive the adsorption isotherm of nitrogen atoms on molybdenum glass, the type of recombination mechanism and the dependence of the activation energy for desorption (or the heat of adsorption) on the fractional coverage.

The purpose of this paper is to provide a comprehensive overview of the fieldeffect transistor (FET) small-signal modeling using artificial neural networks (ANNs). To gain an in-depth insight into how to effectively develop an ANN model, we present a comparative study on the application of the ANNs for modeling the scattering (S-) parameters of a variety of FET technologies versus bias point, ambient temperature, and geometrical dimensions. As will be shown, the main challenge consists of identifying the most appropriate ANN model for the specific case under study. This is because the performance of an ANN-based model can vary significantly, depending especially on the choice of the model structure and the size and parameters of the chosen ANN. In addition, the choice of the model is related directly to the behavior of the FET characteristics, which might greatly depend on the selected device technology and operating conditions. The analysis of the present comparative study allows understanding how to properly construct ANN models to perform at their best for a successful FET modeling.

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Gallium nitride high electron-mobility transistors have gained much interest for high-power and high-temperature applications at high frequencies. Therefore, there is a need to have the dependence on the temperature included in their models. To meet this challenge, the present study presents a neural approach for extracting a multi-bias model of a gallium nitride high electron-mobility transistors including the dependence on the ambient temperature.\ud Accuracy of the developed model is verified by comparing modeling results with measurements

Neural networks are proposed for efficient temperature-dependent modeling of small-signal and noise performances of low-noise microwave transistors over a wide temperature range. The proposed models can be based either on neural networks only or on a combination of neural networks and empirical transistor models.